LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network - Jiang, Zhigang; Xiang, Zhongzheng; Xu, Jinhua; Zhao, Ming (Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online), 2022)
3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizondepth along with room height can obtain omnidirectionalgeometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planar-geometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG-Transformer to model geometry relations. SWG-Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets. The code is publicly available at https://github.com/zhigangjiang/LGT-Net.